Bayesian inference for treatment effects under nested subsets of controls

When constructing a model to estimate the causal effect of a treatment, it is necessary to control for other factors which may have confounding effects. Because the ignorability assumption is not testable, however, it is usually unclear which set of controls is appropriate, and effect estimation is generally sensitive to this choice. A common approach in this case is to fit several models, each with a different set of controls, but it is difficult to reconcile inference under the multiple resulting posterior distributions for the treatment effect. Therefore we propose a two-stage approach to measure the sensitivity of effect estimation with respect to control specification. In the first stage, a model is fit with all available controls using a prior carefully selected to adjust for confounding. In the second stage, posterior distributions are calculated for the treatment effect under nested sets of controls by propagating posterior uncertainty in the original model. We demonstrate how our approach can be used to detect the most significant confounders in a dataset, and apply it in a sensitivity analysis of an observational study measuring the effect of legalized abortion on crime rates

High-Risk Corneal Graft Rejection in the Setting of Previous Corneal Herpes Simplex Virus (HSV)-1 Infection

Acknowledgments The authors thank M. Robertson and R. Fordyce for technical support during the duration of the study. The work performed in Aberdeen was supported by grant from Action Medical Research UK (SP4328; London, England, UK), NHS Grampian Endowment grant (12/49; Aberdeen, Scotland, UK), and Saving Sight in Grampian (Charity No.SC002938; Aberdeen, Scotland, UK). The work performed in Pittsburgh was supported by a Fight for Sight Post-Doctoral Award (JEK; New York, NY, USA); unrestricted grants from the Western Pennsylvania Medical Eye Bank Foundation (Pittsburgh, PA, USA), Research to Prevent Blindness (New York, NY, USA), and the Eye and Ear Foundation of Pittsburgh (RLH; Pittsburgh, PA, USA); and National Institutes of Health Grants P30EY08098 (RLH; Bethesda, MD, USA) and EY10359 (RLH).Peer reviewedPublisher PD

Interpersonal violence in peacetime Malawi.

Background: The contribution of interpersonal violence (IPV) to trauma burden varies greatly by region. The high rates of IPV in sub-Saharan Africa are thought to relate in part to the high rates of collective violence. Malawi, a country with no history of internal collective violence, provides an excellent setting to evaluate whether collective violence drives the high rates of IPV in this region. Methods: This is a retrospective review of a prospective trauma registry from 2009 through 2016 at Kamuzu Central Hospital in Lilongwe, Malawi. Adult (\u3e16 years) victims of IPV were compared with non-intentional trauma victims. Log binomial regression determined factors associated with increased risk of mortality for victims of IPV. Results: Of 72 488 trauma patients, 25 008 (34.5%) suffered IPV. Victims of IPV were more often male (80.2% vs. 74.8%; p Discussion: Even in a sub-Saharan country that never experienced internal collective violence, IPV injury rates are high. Public health efforts to measure and address alcohol use, and studies to determine the role of mob justice, poverty, and intimate partner violence in IPV, in Malawi are needed. Level of evidence: Level III